About Pro-CaRE
Professional Career Recommendation Engine
Our Mission
Pro-CaRE (Professional Career Recommendation Engine) is an automated, Artificial-Intelligence (AI)-enabled recommendation system designed to engage undergraduate engineering students in crafting optimal career paths through internship experiences. The purpose of this web-based system is to facilitate the internship advising and matching processes.
The Challenge
While the value of internship experiences in engineering education is well established, what is often missing is cohesive, proactive advising that assists students in identifying and selecting opportunities that match their cognitive, non-cognitive, and personal backgrounds. Furthermore, traditional internship recommendation systems fail to explain why recommendations are made and how these opportunities can be beneficial for their career building.
Our Approach
Based on this understanding, Pro-CaRE is developed on inclusive and universal learning design principles, emphasizing the importance of explainable recommendations. By doing so, the system helps engineering students not only identify job searches but also understand why a particular recommendation might be the best opportunity for them.
Developing these resources and making them accessible to all students enhances fairness in providing experiential learning opportunities, thereby increasing the diversity and retention of the engineering student population.
Smart Recommendations
Receive tailored internship recommendations that match your skills, interests, and career goals.
Explainable AI
Understand why specific opportunities are recommended and how they benefit your career development.
Inclusive Design
Built on principles that ensure all students have equal access to career-enhancing opportunities.
Key Features
Personalized Recommendations
Get internship suggestions tailored to your academic background, skills, and career interests.
Explainable Results
Understand why certain internships are recommended and how they align with your profile.
Skill Gap Analysis
Identify areas for improvement to better qualify for desired internship positions.
Application Streamlining
Quickly apply to relevant internship positions with guided application processes.
Research Team
Dr. Jinnie Shin
Dr. Shin has expertise in application of theory-based natural language processing and learning analytics in education research. Her work focuses on bridging the gap between psychometric analysis and artificial intelligence in education research.
Dr. Kent Crippen
Dr. Crippen's research embraces the grand challenge of providing an inclusive and robust STEM workforce through the design, development, and evaluation of cyberlearning environments. His work focuses on addressing the under-representation of specific populations in STEM and understanding how learning occurs in particular settings.
Dr. Bruce F. Carroll
Dr. Carroll specializes in fluid mechanics, experimental methods, and applied AI and data analytics. His work extends to personalized learning, experiential learning, and building a culture of inclusion and innovation in engineering education.
Woorin Hwang
Woorin's research focuses on AI integration into educational systems, personalized/adaptive learning, and explainable AI recommender systems in education. She brings experience as an instructional specialist and content developer.
Hongming (Chip) Li
Chip's research focuses on Learning Analytics, Educational Data Mining, and AI in education, particularly developing human-centered AI systems for education.
Publications
Woorin Hwang, Hongming Li, Anna Pauline Aguinalde, Yilin Zhang, Jinnie Shin, Kent Crippen, & Bruce Carroll. (2025). Building Explainable Recommender System for Engineering Students Work-Integrated Learning (WIL). Proceedings of the 18th International Conference on Educational Data Mining, 549–553. https://doi.org/10.5281/zenodo.15870223